Reliability and Validity Study of the Mobile Learning ... · that the more an innovation is tried, the faster it is adopted. Finally, observability is the degree to which the results

International Journal of Education in Mathematics, Science and Technology

Volume 2, Number 4, October 2014, Page 300-316 ISSN: 2147-611X

Reliability and Validity Study of the Mobile Learning Adoption Scale

Developed Based on the Diffusion of Innovations Theory

Ismail Celik*, Ismail Sahin, Mustafa Aydin

Necmettin Erbakan University

Abstract

In this study, a mobile learning adoption scale (MLAS) was developed on the basis of Rogers’ (2003) Diffusion

of Innovations Theory. The scale that was developed consists of four sections. These sections are as follows:

Stages in the innovation-decision process, Types of m-learning decision, Innovativeness level and attributes of

m-learning. There is one question at the level of classification regarding the investigated characteristics of the

participants in the first three sections of the scale. The last section of the scale is composed of 18 items and 5

sub-dimensions in the 7-item Likert type. MLAS was developed in three stages. In the first stage, a detailed

review of literature was performed and an item pool was formed. In the second stage, explanatory factor

analysis was performed to determine the factor structure of the scale while confirmatory factor analysis was

performed to test whether the factors formed confirmed the theory or not. In the final stage of the study, the

reliability of the scale was determined through item, test-retest reliability and internal consistency (Cronbach

Alpha) analyses. In conclusion, the scale developed within the scope of this study was shown to yield valid and

reliable scores.

Key words: Adoption of m-Learning, Diffusion of Innovations Theory, Scale development.

Introduction

In today’s globalized world, the role of technology in the dissemination of information in the fields of formal

and informal learning is in an ever increasing state (Male & Pattinson, 2011). With this role, widespread use of

technology and constant dissemination of information have paved the way for distance learning, electronic

learning (e- learning), and mobile learning (m-learning) (Kuşkonmaz, 2011).

Researchers and educators have a significant role in revealing the use of mobile technologies as a learning tool

(Ng & Nicholas, 2012), which is one of the fastest developing areas of technology (Clough, Jones, McAndrew,

& Scanlon, 2008). Mobile devices offer learners convenient access to information regardless of time and place.

The use of mobile technologies in education helps learners acquire the right content, at the right time and at the

right place (Wagner, 2005; Waycott, Jones, & Scanlon, 2005). In addition, the use of mobile devices encourages

and supports learning opportunities (Hsu et al., 2006) through relevant forms of access to information (Smordal

& Gregory, 2003) and assessment tools for students (Kneebone et al., 2003).

In the literature, m-learning can be defined as learning anywhere and anytime with a mobile device (Attewell,

2005; Geddes, 2004). Traxler (2005) regards m learning as any educational process where dominant technology

involves mobile devices, whereas Colazzo et al. (2003) defines m-learning as any teaching and learning activity

that can be accomplished via mobile devices.

Owing to their advanced features, mobile technologies make people’s life easier (Yılmaz, 2011). With the use of

mobile technologies in education, students and teachers can engage in different motivating and interesting

activities (Cheon, Lee, & Crooks, 2012). When the relevant literature is examined, the advantages of mobile

learning can be listed as follows: It attracts learners’ attention, is efficient and effective, saves time and money,

increases cooperation among learners, can be implemented through a simple design composed of small and

different pieces of information, can be dynamically updated, is personal and allows access to information via

different sources (Chen, Kinshuk, Wei, & Yang, 2008; Nassoura, 2012; Özdamar Keskin, 2011; Pinkwert et

* Corresponding Author: Ismail Celik, [email protected]

301

IJEMST (International Journal of Education in Mathematics, Science and Technology)

al.,2003; Woodill, 2011). Despite the opportunities mobile technologies offer, it has been argued that the use of

mobile devices in the educational process is not at the desired level (Hwang & Tsai, 2011).

A scrutinization of the relevant m-learning literature reveals that many studies have focused on how and why

university students adopt m-learning. Technology Acceptance Model, Theory of Reasoned Action and The

Theory of Planned Behavior are some of the theories taken as bases in the adoption of m-learning. A large

majority of the researchers have investigated the adoption of m-learning within the framework of Technology

Acceptance Model (Akour, 2009; Chen & Huang, 2010; Huang et al., 2012; Liu et al., 2010; Mahat et al., 2012;

Park et al., 2012; Tan et al., 2012). In these studies, especially perceived usefulness and personal innovation are

emphasized as two important factors in the process of adoption of m-learning (Liu et al., 2010; Cheon, 2012). In

studies based on Unified Theory of Acceptance and Use of Technology (Venkatesh & Davis, 2000),

performance expectancy, effort expectancy, social influence, perceived playfulness and self-management of

learning were all significant determinants of behavioural intention to use m-learning (Wang et al., 2009;

Nassoura, 2012; Donaldson, 2011). In a study based on Theory of Planned Behavior, on the other hand, (Cheon,

2012), attitude, subjective norm and behavioral control were found to be three major factors in the process of

adoption of m-learning.

Technology Acceptance Model, Theory of Reasoned Action, and The Theory of Planned Behavior are all based

on social psychology and investigate adoption of innovation usually at the individual level by bringing to the

fore internal decision making processes (Fishben & Ajzen, 1975; Ajzen, 1991; Davis, 1989). Diffusion of

Innovations Theory, on the other hand, is focused on the features of what is new and investigates diffusion of

the innovation within the system (Moore & Benbasat, 1991; Rogers, 2003). Some studies conducted on the basis

of technology acceptance models are limited to investigation of user attitudes concerning functionality and

nature of m-learning (Benbasat & Barki, 2007). However, m-learning is a totally new learning process (Cheon,

2012) and should be investigated from different perspectives. It is important to regard m-learning as an

innovation and reveal factors determining its diffusion across educational settings. When relevant literature is

examined, there is currently no study investigating the adoption of m-learning based on Rogers’ Diffusion of

Innovations Theory.

Theoretical Framework: Diffusion of Innovations

The process of adoption of innovations has been a subject of investigation for more than 30 years and the model

Rogers proposed to in his book, entitled Diffusion of Innovations, is one of the most widely adopted models (Li

& Sui, 2011; Sherry & Gibson, 2002). Many researchers have argued that the diffusion of innovations theory is

the best theory for studies conducted in higher education and educational environments (Medlin, 2001; Parisot,

1995; Sahin, 2006). Rogers (2003) stated that since many diffusion studies include technological diffusion, the

words technology and innovation can be used interchangeably. In the Diffusion of Innovations Theory, the

factors affecting diffusion were investigated in terms of individuals’ perceptions and how those innovations

would diffuse within the social system through communicative processes (Rogers, 2003).

Attributes of innovations

Within the theory, attributes of innovations contain 5 characteristics of innovation. (1) relative advantage, (2)

compatibility, (3) complexity, (4) trialability and (5) observability. Relative advantage means the degree to

which an innovation is perceived to be better than the idea it supersedes. According to Rogers (2003), the

relative advantage can be measured in economic terms, social prestige, convenience and satisfaction.

Compatibility indicates the degree of compatibility among the values, past experiences and potential needs of

adopters of an innovation. Ease of use (as opposed to complexity), in Rogers’s definition, refers to the extent to

which an innovation is perceived as easy to understand and use. Agarwal and Prasad (1997) also defined ease of

use as the perception by a potential adopter of the use of an innovation as easy. As for triability, it refers to “the

degree to which an innovation may be experimented with on a limited basis” (Rogers, 2003, p. 16), meaning

that the more an innovation is tried, the faster it is adopted. Finally, observability is the degree to which the

results of the use of an innovation can be observed by the adopters.

Types of innovation decision

There are three types of innovation decisions affecting diffusion of innovations in a social structure, which are

namely innovation decision made by the individual himself/herself (optional), innovation decision made under

the influence of others (collective) and innovation decision made under the influence of an authority (authority)

302 Celik, Sahin & Aydin

(Rogers, 2003). In the innovation decision made by the individual himself/herself, decision of adoption or

rejection is made independently of the social system. In the innovation decision made under the influence of

social system, or environment, (collective), decision of adoption or rejection is made by consensus of members

of the system. In the decision of adoption or reject of the innovation made under the pressure of an authority, on

the other hand, the decision is made by a group that possesses power, status or technical expertise (Rogers,

2003).

Innovativeness levels

According to Rogers (2003), innovativeness is “the degree to which an individual or other unit of adoption is

relatively earlier in adopting new ideas than other members of a system” (p. 242). Rogers (2003) categorized

adopters into five on the basis of innovativeness: innovators, early adopters, early majority, late majority, and

laggards.

Figure 1. Adopter Categorization on the Basis of Innovativeness

As can be seen in Figure 1, the distribution of adopters is a normal distribution. In his studies, Rogers found that

members of a social system were in compliance with the normal distribution. Moreover, Rogers (2003) noted

that incomplete adoption and non-adoption do not form this adopter classification. Only adopters of successful

innovations generate this curve over time. In this normal distribution, each category is defined using a

standardized percentage of respondents.

Innovators take an interest in new ideas and lead cosmopolitan lives far from the norms of the society where

they live. Early adopters are better integrated into the social system in comparison with innovators. They adopt

new ideas in their initial stages, thereby helping reduce uncertainties in this regard, and convey their subjective

judgments about the innovation to their immediate environment through interpersonal communication. The

Early Majority’s innovation decision process takes longer when compared with innovators and early adopters.

They would not like to be either the first one or the last one to try an innovation and they attach importance to

the ideas of their inner circle (Rogers, 2003). Late majority, whose most important characteristic is to approach

innovations with suspicion, begin the adoption process after a large majority of the society has adopted the

innovation. Individuals in this group do not like taking risks and are conservative. Laggards are the last ones in a

social system to adopt innovations. As traditionalists, laggards consult with those who have traditional values

like themselves.

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Stages of innovation-decision process

According to the Diffusion of Innovations Theory (Rogers, 2003), innovation decision process consists of five

stages: (1) knowledge, (2) persuasion, (3) decision, (4) implementation, and (5) confirmation.It is a process that

begins with an individual’s getting information about the innovation (knowledge), and continues with

developing an attitude towards the innovation (persuasion), deciding to adopt or reject the innovation (decision),

implementing the innovation (implementation) and confirming the decision to implement the innovation.

Given that mobile technologies are considered innovations that have an impact on the society in general and on

educational settings in particular, the Diffusion of Innovations Theory can be used as a framework to further

investigate the adoption of mobile devices for learning purposes. Thus, the purpose of this study is to develop a

mobile learning adoption scale based on Rogers’ Diffusion of Innovations Theory.

Methodology

The m-Learning Adoption Scale (MLAS) is composed of four sections. The first section is intended to reveal

the participants’ decision stage concerning the adoption of m-learning. The second section of the scale is aimed

at revealing the participants’ type of decision regarding the adoption of m-learning, whereas the third section

aims to determine the features of innovativeness in the adoption of m-learning. The last section of the scale

consists of items including the Attributes of m-learning as an innovation as described in the Diffusion of

Innovations Theory (Rogers, 2003).

Study Group

The study group is composed of preservice teachers majoring in different departments in the college of

education at a university. Four different study groups were formed in order to determine the validity and

reliability of the relevant scale. The number of individuals in the group formed for the Explanatory Factor

Analysis (EFA) was 205. 95 of the participants in this group were male (46.3 %), whereas 110 were female

(53.7 %). On the other hand, the number of participants in the group formed for the Confirmatory Factor

Analysis (CFA) was 266. 121 of the participants in this group were male (45.5 %), whereas 145 were female

145 (55.5 %). The group that was formed to determine the test-retest reliability was composed of 59 people. 34

of the participants in this group were male (57.6 %), while 25 were female 25 (42.4 %). Finally, the group that

was formed for the linguistic validity of the relevant scale consisted of 32 people. 19 of the group in question

were male (59.3 %) whereas 13 were female (41.7 %).

Validity Analyses

Content Validity

In the first three sections of the scale (decision stage, decision type and of innovation), there are questions at the

level of classification. There are six choices in the question intended to determine the participants’ decision

stage regarding adoption of m-learning. 1=knowledge, 2=persuasion, 3=decision (accept or reject),

4=implementation and 5=confirmation. High scores obtained from this section indicate that the person in

question is at a higher level at the decision stage. A question with three choices was asked to determine the

decision type regarding adoption of m-learning. The choices are, 1=decision based on an authority, 2=decision

based on one’s environment and 3=individual’s own decision. An increase in the scores obtained from this

section means an individual’s decision to adopt m-learning is independent of authority or environment. There

are five choices in the question prepared to determine the participants’ characteristics of innovativeness. The

relevant choices are: 1=laggards, 2=late majority, 3=early majority, 4= early adopters and 5=innovators. High

scores obtained from this section indicate that the individual’s innovative characteristic is also high.

In order to develop the last part of the m-learning adoption scale (attributes regarding the adoption of m-

learning), a detailed review of literature was conducted within the scope of Rogers’ (2003) Diffusion of

Innovations Theory. Then, a 32-item draft scale was prepared under 5 factors, namely “Relative Advantages”,

“Compatibility”, “Trialability”, “Complexity”, and “Observability”. The draft form was reviewed by three

expert academicians in the field of instructional technology and two expert academicians in the field of

curriculum development. In accordance with the feedback and suggestions received concerning the

comprehensibility of the items, complexity of the statements and their compliance with the Diffusion of

Innovations Theory, the items in the scale were revised and rewritten. 18 items that were approved by at least


four experts were included in the scale. The items in this section are of the 7-point Likert type. The choices in

the relevant items range from 1=I totally Disagree to 7=I Totally Agree.

Construct Validity

Factor Analysis can be defined as a multivariate statistic intended to find or discover a limited number of

unrelated and conceptually significant new factors or dimensions by bringing together p-numbered interrelated

variables (Büyüköztürk, 2012). Factor analysis is divided into two, namely explanatory factor analysis and

confirmatory factor analysis. In this study, EFA was conducted in order to determine the factor structure of the

first section of the scale. EFA is a factor analysis technique performed to determine the number of latent

variables in a scale and the factor structure underlying these variables instead of testing hypotheses (Shur,

2006). Kaiser-Meyer-Olkin (KMO) and Barlett tests were conducted to determine whether factor analysis could

be performed on the dataset. Statistical Package for Social Sciences (SPSS) 17.0 was used for EFA, KMO and

Barlett tests. Confirmatory Factor Analysis (CFA) was performed in order to determine whether or not the

factor structure of the relevant section in the original form could be confirmed. Sümer (2008) defined CFA as an

analysis intended to determine to what extent factors formed from various variables deriving from a theoretical

basis complied with actual data. Since a model whose factors were derived from a theory was being tested, a

confirmatory maximum likelihood factor analysis was perfomed using AMOS 19.0.

Linguistic Validity

The translation-retranslation method, which is recommended in the relevant literature, was performed for the

linguistic validation of the scale developed (Kevrekidis et al., 2008; Sahin, 2010). First of all, the scale was

translated into English by three academics in the English Language and Literature Department independently of

the authors of the study. Then, the English version of the scale was translated back into Turkish by an academic

who has a good command of both languages. To give the scale its final form, the two translations were

compared and contrasted and necessary changes were made. The English and Turkish versions of the scale were

administered to the student teachers attending their senior class in the English Language and Literature

Department twice with a three-week interval.

Reliability Analyses

The reliability analysis of the scale developed within the scope of the study was determined through item, test-

retest reliability and internal consistency (Cronbach Alpha) analyses.

Item Analysis

Item-total correlation explains the relationship between the scores obtained from the test items and the total

score of the test. A positive and high item total correlation indicates that the items exemplify similar behaviors

and the internal consistency of the scale is high (Büyüköztürk, 2012). Another way of conducting item analysis

is determining the differences between the top 27% and bottom 27% item score averages that emerged as a

result of the test total scores using unrelated t test. If the differences between the groups are, as anticipated,

significant, then this is an indication of internal consistency of the test (Büyüköztürk, 2012).

Test-Re-Test Reliability Analysis

This is explained through the correlation between the data obtained by administering a test twice to the same

group at certain intervals. The degree of the correlation between the two data sets is calculated by using the

correlation coefficient. The correlation coefficient is used to interpret to what extent the scores generated by a

scale in different times are consistent (Büyüköztürk, 2012; Büyüköztürk et al., 2012).

Internal Consistency (Cronbach Alpha) Analysis

The internal consistency of the six sub-dimensions included in the first section of the scale was investigated

using the Cronbach Alpha value. SPSS 17.0 program was used for these calculations.

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Findings and Discussion

Findings about participants’ m-learning decision types, decision stages and characteristics of

innovativeness

The participants’ decision stages concerning m-learning is given in Table 1.

Table 1. m-Learning Decision Stage

Sub Dimension Choice n %

Knowledge

I do not have much knowledge about M-

learning. I usually see learning

applications on mobile devices in people

around me.

78 16.5

Persuasion

I think the use of mobile devices in the

educational process is beneficial. I am

conducting research regarding learning

via mobile devices.

97 20.6

Decision

Adoption

In the time ahead, I will receive the

knowledge I need using the opportunities

provided by mobile devices.

74 15.7

Rejection

I do not think that I will

obtain the news I need using mobile

devices.

32 6.8

Implementation

I learn the knowledge I need using mobile

devices. I use mobile devices in

educational activities.

91 19.3

Confirmation

I have realized that learning through

mobile devices is beneficial for me. I

think mobile devices can be used

effectively in educational activities.

99 21

According to Table 1, of the 471 student teachers, 16.5 % (n=78) were at the knowledge stage, 20.6 % (n=97)

were at the persuasion stage, 15.7 % (n=74) at the decision/adoption stage, 6.8 % (n=32) were at the

decision/rejection stage, 19.3 % (n=91) were at the implementation stage whereas 21 % (n=99) were at the

confirmation stage.

The participants’ decision types regarding the adoption of m-learning are given in Table 2.

Table 2. m-Learning Decision Type

Sub

Dimension Choice f %

Authority

I used m-learning applications under the influence of

people in authority. Social pressure was effective in my

decision to use m-learning applications. I obtained

information about the educational uses of mobile devices

due to a demand by an authority (directors or people in

authority).

64 13.6

Environment

Since important people around me used mobile devices, I

used these devices for the purpose of learning. My

friends’ using mobile devices encouraged me to use these

devices in education. My family supported in my using

mobile devices for educational purposes.

188 39.9

Self Learning the knowledge I need using mobile devices was

entirely of my own accord. 214 46.4


When Table 2 is examined, it is seen that of the 471 student teachers, 64 (13.6 %) decided to learn under the

influence of an authority, 188 (39.9 %) decided under the influence of social pressure while 214 (44.6 %)

decided on their own.

Distribution of 471 student teachers according to their innovativeness is given in Figure 2.

Figure.2. Participants’ Innovativeness

When Figure 2 is examined, it is observed that 12.3 % of the participants are Laggards (n=58), 25.9 % are late

majority (n=122), 46.5 % are early majority (n=219), 8.1 % are early adopters (n=38) and 7.2 % are innovators

(n=34) (Skewness:0.295; Std.Dev:1.02; Kurtusosis:0.06). In the relevant literature, it has been stated that

distributions where skewness coefficient is between -1 and +1 are normal distributions (Büyüköztürk, 2012).

Participants’ innovativeness exhibits a normal distribution as proposed by the Diffusion of Innovations Theory

(Rogers, 2003). The groups with the highest percentages according to Rogers’ classification are Early Majority

and Late Majority (68 %). In the classification of innovativeness regarding m-learning, Early Majority and Late

Majority have percentages similar to those in the theory (72.1 %). Moreover, according to Rogers, the group

with the lowest percentages in the innovativeness classification involves innovators. In the m-learning

innovativeness classification, the group with the lowest percentage is innovators with a share of 7.2 %.

Table 3. m-Learning Innovativeness

Sub Dimension Choice f %

Laggards

I am one of the last to use m-learning applications in my environment / I

have not yet used a mobile device to learn new information. 58 12.3

Late Majority

In the learning process, I started to use mobile devices much later than

many other people around me. 122 25.6

Early Majority

I was not one of the first to use mobile devices in the learning process but I

used them earlier than many others around me. 219 46.5

Early Adopters

When mobile devices were becoming widespread around me, I was one of

the first to learn the knowledge I needed using mobile devices. 38 8.1

Innovators

I began to use m-learning applications when those around me did not have

any information about these applications and there were not enough mobile

devices around.

34 7.2

Findings about Construct Validity

KMO and Barlett tests were conducted to determine whether the data obtained were suitable for making factor

analysis. For the data to be suitable for factor analysis, KMO should be above.60, and the Bartlett test should be

significant (Büyüköztürk, 2012). According to the data in the study, KMO value was .93 and Bartlett test result

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was significant (χ2=6083.7; p<.001). On the basis of these findings, it was concluded that the data were eligible

for factor analysis. Principal components analysis technique and varimax rotation method were used in the

exploratory factor analysis. As a result of the factor analysis, it was found that 18 items constituted a five-factor

structure whose Eigenvalue was greater than 1. Eigenvalues, variance percentages and total variance

percentages belonging to the 5 factors that emerged are given in Table 4.

Table 4. Factor Structure of m-Learning Characteristics

Factor Eigenvalue

Percentage

of

Variance

Percentage of Total

Variance

1 3.99 19.01 19.01

2 3.34 15.92 34.93

3 2.69 12.84 47.77

4 2.56 12.21 59.99

5 1.57 7.50 67.49

When Table 4 is examined, it is seen that the rate of variance each factor explains varies between 7.50 and

19.01, and the total variance explained is around 67.49 %. As a result of the principal components analysis, the

emerging factor loadings of the items in the scale are given in Table 5.

Table 5. Item Factor Loads Regarding Adoption of m-Learning

Sub Dimension Factor Loads

Items 1 2 3 4 5

Relative Advantage

RA1 0.881

RA2 0.913

RA3 0.888

RA4 0.796

Compatibility

CM1 0.667

CM2 0.769

CM3 0.839

CM4 0.873

Trialability

TR1 0.901

TR2 0.911

TR3 0.512

Complexity

EU1 0.626

EU2 0.890

EU3 0.880

Observability

OB1 0.727

OB2 0.879

OB3 0.866

OB4 0.809

When Table 5 is examined, it is seen that factor loadings of the items in the scale vary between 0.512 and 0.913.

In order to test the five-item structure of the scale, which emerged as a result of the exploratory factor analysis

and find out to what extent the exploratory model was appropriate (Tabachnick & Fidell, 2007), confirmatory

factor analysis was performed on the dataset obtained from 236 participants. As a result of the analysis

conducted, goodness of fit indices of the 5-factor model were examined and it was found that chi-square value

(χ2=337,534 sd= 117, χ2/sd=2,89 p=0.00) was significant. In confirmatory factor analysis, if the χ2/sd rate

obtained is smaller than 3, then this shows that the model has favorable goodness of fit values (Kline, 2005;

Tabachnick & Fidell, 2007). In this sense, it was observed that the proposed 5-factor model was highly

compatible with the data. When the other indices included in the model were examined, it was seen that GFI

value was .92, CFI value was .95, IFI value was .96, RMR value was .05 and RMSEA value was .066. The

values obtained for the specified indices are regarded as indicators of good fit values in model studies (Kline,

2005; Tabachnick and Fidell, 2007). The findings indicate that the five factor structure of the scale has been

confirmed as a result of the confirmatory factor analysis.

As can be seen from Figure 2, the path coefficients in the model vary between .37 and .90. All of these values

are above .30, which is regarded as a breakpoint (Kline, 2005). The path coefficients of the scale vary between


.70 and .81 in the relative advantage dimension, between .50 and .72 in the compatibility dimension, between

.70 and .85 in the observability dimension, .37 and .88 in the complexity (simplicity) dimension and between

.67 and .85 in the trialibility dimension.

Figure. 3. CFA results of the five-factor model (χ2=451,603 sd= 167, χ2/sd=2,70, p<0.001)

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Findings Regarding the Reliability Analysis

Item-total correlation and 27 % top-bottom group comparisons were performed in order to determine the item

discrimination of the scale developed. The findings obtained are shown in Table 6.

Table 6. 27 % Top-Bottom Groups Comparison and Item-Total Correlation for Items of m-Learning

Characteristics

Items Item-total correlation

Bottom Group

27 %

Top Group

27 %

Comparison of Top

and Bottom Groups

r S S t m-LA1 ,721 3,33 1,35 6,22 0,98 -18,85

**

m-LA2 ,774 3,43 1,21 6,35 0,93 -20,78**

m-LA3 ,715 3,44 1,23 6,23 1,00 -19,11**

m-LA4 ,673 3,81 1,52 6,32 1,06 -14,71**

m-LA5 ,543 2,97 1,33 5,22 1,68 -11,42**

m-LA6 ,726 3,23 1,30 6,03 1,06 -18,13**

m-LA7 ,720 2,93 1,36 6,09 1,12 -19,48**

m-LA8 ,777 2,91 1,30 6,32 0,88 -23,66**

m-LA9 ,602 3,26 1,53 6,04 1,22 -15,47**

m-LA10 ,642 3,32 1,47 6,08 1,23 -15,61**

m-LA11 ,541 4,19 1,74 6,16 1,12 -10,36**

m-LA12* ,359 4,54 1,47 5,93 1,68 -6,77

**

m-LA13 ,715 3,48 1,29 6,33 0,99 -19,02**

m-LA14 ,733 3,58 1,30 6,52 0,77 -21,10**

m-LA15 ,681 3,18 1,42 5,99 1,30 -15,90**

m-LA16 ,747 2,76 1,25 6,08 1,08 -21,87**

m-LA17 ,722 2,86 1,16 5,93 1,20 -19,96**

m-LA18 ,602 2,82 1,24 5,26 1,53 -13,43**

** p<0.01, *reverse item

When Table 6 is examined, it is seen that t values that were obtained vary between -23.66 and -6.77. It is also

observed that the scores derived from each item are significantly correlated with item-total correlation. The

Cronbach Alpha internal consistency coefficient of the mobile learning adoption scale calculated on the basis of

item analysis was 0.94 for the whole scale. The Cronbach Alpha coefficients calculated for the subdimensions

of the scale are as follows: Relative Advantages 0.892; Compatibility, 0.797; Trialability, 0.795; Complexity,

0.725; and Observability 0.839. When the relevant literature is examined, for scales used in research, the level

of an acceptable Cronbach’s alpha coefficient is suggested as 0.70 (Anastasi, 1982; Tavsancil, 2002; Sahin,

2010). Thus, it can be concluded that all five subdimensions of the scale are reliable.

Findings about the Test-retest Reliability Analysis

In order to further investigate the reliability of the scale through test-retest method, the scale was administered

twice to a sample of 59 students at an interval of two weeks. Pearson correlation coefficient between the scores

of the two administrations was found to be 0.87 (p<.01). Hence, it is concluded that consistent (decisive)

measurements can be made using the adapted scale at different administrations.

Findings about Linguistic Validity

For linguistic validity, the Turkish and English Versions of the scale were administered to a group of 34 people

at an interval of three weeks. Pearson correlation coefficient between the scores obtained from these two

versions was found to be 0.91 (p<.01). According to this finding, it can be said that the linguistic validity of the

English version of the scale was ensured.


Conclusion

In this study, an m-learning adoption scale was developed on the basis of Rogers’ (2003) Diffusion of

Innovations Theory. The scale consists of four sections. These sections are as follows: Stages in the innovation-

decision process, Types of m-learning decision, Innovativeness level and Attributes of m-learning. The last

section of the scale is composed of 18 items and 5 sub-dimensions and uses a 7-point Likert type rating scale.

MLAS was developed in three stages. In the first stage, a detailed review of literature was performed and an

item pool was formed. An expert in instructional technology was consulted in order to ensure content validity

for the items created. The items of the scale were reviewed in accordance with the feedback and suggestions

received from the expert. In the second stage, EFA was performed to determine the factor structure of the scale

while CFA was performed to test whether the factors confirmed the theory or not. KMO and Barlett’s tests were

calculated in order to determine whether the data for EFA were eligible for factor analysis or not. KMO value

was high and Barlett’s test was significant, which indicated that the data were suitable for conducting the factor

analysis. At this stage, CFA was performed for the scale using the Maximum Likelihood method. The model

formed for CFA seemed to have acceptable goodness of fit indices. As a result of the CFA, it was concluded

that the dimensions included in the scale confirmed the structure in Rogers’ Diffusion of Innovations Theory.

In the final stage of the study, the reliability of the scale was determined through item, test-retest reliability and

internal consistency (Cronbach Alpha) analyses. The fact that the item total correlation for the m-learning

adoption scale was positive and high indicated that the items represented similar behaviors and that the internal

consistency of the scale was high. Also, 27 % top-bottom groups comparisons were made in order to determine

the item discrimination of the scale. The fact that the expected scores between the groups were significant is an

indication of the internal consistency of the scale. Thus, it was seen that the scale discriminated the individuals

in terms of the attribute measured. Moreover, the high correlation coefficient calculated for the test-retest

reliability indicates that consistent measurements can be obtained from the scale in different administrations. In

conclusion, the scale developed within the scope of this study was shown to yield valid and reliable scores.

Acknowledgements

The scale developed in this study can be used by other researchers with the appropriate citation. There is no

requirement to get permission from the authors.

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313


APPENDIX.1

MOBILE LEARNING (M-LEARNING) ADOPTION SCALE

m-learning Decision Stage

Please mark the best choice with an X. Choice

I do not have much knowledge about m-learning.

I usually see learning applications on mobile devices in people around me.

I think the use of mobile devices in the educational process is beneficial. I am conducting research

regarding learning via mobile devices.

I will learn the knowledge I need in the future using the opportunities provided by mobile devices.

I do not think that I will

obtain the news I need using mobile devices.

I learn the knowledge I need using mobile devices. I use mobile devices in educational activities.

I have realized that learning through mobile devices is beneficial for me. I think mobile devices can

be used effectively in educational activities.

m-Learning Decision Type


I used m-learning applications under the influence of people in authority. Social pressure was

effective in my decision to use m-learning applications. I obtained information about the educational

uses of mobile devices due to a demand by an authority (directors or people in authority).

Since important people around me used mobile devices, I used these devices for the purpose of

learning. My friends’ using mobile devices encouraged me to use these devices in education. My

family supported in my using mobile devices for educational purposes.

Learning the knowledge I need using mobile devices was entirely of my own accord.

m-Learning Attribute of Innovativeness


I am one of the last to use m-learning applications in my environment / I have not yet used a mobile

device to learn new information.

In the learning process, I started to use mobile devices much later than many other people around

me.

I was not one of the first to use mobile devices in the learning process but I used them earlier than

many others around me.

When mobile devices were becoming widespread around me, I was one of the first to learn the

knowledge I needed using mobile devices.

I began to use m-learning applications when those around me did not have any information about

these applications and there were not enough mobile devices around.


Mobile Learning (m-Learning) Adoption Attributes

Specify whether you agree with the following statements or not. 1- means: I totally

disagree and 7-means: I totally disagree.

I to

tall

y d

isa

gre

e

I to

tall

y a

gre

e

1 2 3 4 5 6 7

1. Learning through mobile technologies increases my efficiency.

2. I find m-learning beneficial

3. M-learning increases the quality of what I do.

4. Use of mobile devices contributes to my career.

5. Use of mobile devices is compatible with my traditional teaching methods.

6. Use of mobile devices is compatible with my learning preferences.

7. I possess the knowledge required to use the m-learning method.

8. I use m-learning effectively with my existing knowledge.

9. I possess adequate means to try mobile technologies in the educational

process.

10. I can access m-learning applications any time I like.

11. I first try an m-learning application and then I use it.

12. It is difficult for me to use m-learning applications.

13. It is easy for me to use m-learning applications.

14. It is easy for me to access information I need via mobile devices.

15. I can observe that use of mobile devices for educational purposes benefits

those around me.

16. I can tell others about the benefits of m-learning.

17. I share with those around me the applications about the educational uses of

mobile devices.

18. Those around me help each other in regard to m-learning applications.

315


APPENDIX.2

MOBİL ÖĞRENME (M-ÖĞRENME) BENİMSEME ÖLÇEĞİ

m-Öğrenme Karar Aşaması

Lütfen en uygun seçeneği X ile işaretleyiniz. Seçenek

m-öğrenme hakkında fazla bilgim yoktur.

Mobil cihazlardaki öğrenme uygulamalarını daha çok çevremdekilerde görürüm.

Mobil cihazların eğitim-öğretim sürecinde kullanılmasının faydalı olduğunu düşünüyorum. Mobil

cihazlarla öğrenme konusunda araştırma yapıyorum.

İleriki zamanlarda ihtiyacım olan bilgiyi, mobil cihazların sunduğu imkânları kullanarak

öğreneceğim.

Benim için gerekli olan bilgiyi mobil cihazları kullanarak elde edeceğimi düşünmüyorum.

İhtiyacım olan bilgiyi mobil cihazları kullanarak öğreniyorum.

Mobil cihazları eğitim-öğretim faaliyetlerinde kullanıyorum.

Mobil cihazları kullanarak öğrenmenin bana faydalı olduğunu anladım.

Mobil cihazların eğitim-öğretim faaliyetlerinde etkili bir şekilde kullanılabileceğini düşünüyorum.

m-Öğrenme Karar Türü


m-öğrenme uygulamalarını yetkili kişilerin etkisiyle kullandım. m- öğrenme uygulamalarını

kullanımına karar vermemde çevre baskısının etkisi oldu.

Mobil cihazların eğitsel kullanımı hakkında otorite (idare veya yetkililer) istediği için bilgi sahibi

oldum.

Çevremdeki önemli insanlar, mobil cihazları kullandıkları için ben de bu cihazları öğrenme amaçlı

kullandım. Arkadaşlarımın mobil cihazları kullanması, beni de bu araçları eğitimde kullanmama

teşvik etti. Ailem mobil cihazları eğitsel amaçlı kullanmam hususunda beni destekledi.

Mobil cihazları kullanarak gerekli bilgiyi öğrenmek, tamamen kendi isteğimle olmuştur.

m-Öğrenme Yenilikçilik Özelliği


Çevremde m-öğrenme uygulamalarını en geç kullanan kişilerdendim/ Henüz yeni bir bilgi öğrenmek

için mobil cihaz kullanmadım.

Öğrenme sürecinde mobil cihazları kullanmaya çevremdeki birçok kişiden sonra başladım.

Öğrenme sürecinde mobil cihazları ilk kullanan kişilerden değilim; ancak çevremdeki birçok

kişiden önce kullandım.

Çevremde mobil cihazlar yaygın hale gelirken, kendim için gerekli bilgiyi mobil cihazları

kullanarak öğrenen ilk kişilerden biriydim.

m-öğrenme uygulamalarını kullanmaya, çevremdekilerin çoğu bu uygulamalar hakkında bilgi sahibi

değilken ve çevremde yeterince mobil cihaz yokken başladım.


Mobil Öğrenme (M-Öğrenme) Benimseme Özellikleri

Aşağıda ifadelere katılıp katılmadığınızı belirtiniz. 1-Hiç Katılmıyorum 7-

Tamamen Katılıyorum anlamına gelmektedir.

Hiç

Ka

tılm

ıyo

rum

Ta

ma

men

Ka

tılı

yo

rum

1 2 3 4 5 6 7

1. Mobil teknolojilerle öğrenme, verimliliğimi artırmaktadır.

2. m-öğrenmeyi faydalı bulurum.

3. m-öğrenme yaptığım işin kalitesini artırır.

4. Mobil cihazların kullanılması kariyerime katkı sağlar.

5. Mobil cihazların kullanılması, alışageldiğim öğrenme yöntemleriyle

uyumludur.

6. Mobil cihazların kullanılması, öğrenme tercihlerimle uyumludur.

7. m-öğrenme yöntemini kullanmak için gerekli bilgiye sahibim.

8. Varolan bilgilerimle, m-öğrenmeyi etkin bir şekilde kullanırım.

9. Eğitim-öğretim sürecinde mobil teknolojileri denemek için yeterli imkânlara

sahibim.

10. m-öğrenme uygulamalarına istediğim zaman erişebilirim.

11. Bir m-öğrenme uygulamasını öncelikle dener, daha sonra kullanırım.

12. m-öğrenme uygulamalarını kullanmak benim için zahmetlidir.

13. m-öğrenme uygulamalarını kullanmak bence kolaydır.

14. İhtiyacım olan bilgiye mobil cihazlarla ulaşmak benim için kolaydır.

15. Eğitsel amaçlı mobil teknoloji kullanımının çevremdekilere sağladığı faydaları

gözlemleyebiliyorum.

16. Başkalarına m-öğrenmenin yararlarını anlatabilirim.

17. Çevremdekilerle mobil teknolojilerin eğitsel kullanımına ilişkin uygulamaları

paylaşırım.

18. Çevremdekiler m-öğrenme uygulamalarıyla ilgili birbirlerine destek olurlar.

Documents

Reliability and Validity Study of the Mobile Learning ... · that the more an innovation is tried, the faster it is adopted. Finally, observability is the degree to which the results